最近在训练Yolov8-Pose时遇到一个问题，就是如何将自己使用labelme标注的Json文件转化成可用于Yolov8-Pose训练的txt文件。
具体代码有以下：
1、labelme2coco.py 将自己标注的人体姿态信息json文件格式合并转换成CoCo格式
import os
import json
import numpy as np
import glob
import shutil
np.random.seed(41)
import cv2
#0为背景
classname_to_id = {"person": 1}
class Lableme2CoCo:
def __init__(self, splitDir=''):
self.images = []
self.annotations = []
self.categories = []
self.img_id = 0
self.ann_id = 0
self.splitDir = splitDir
def save_coco_json(self, instance, save_path):
json.dump(instance, open(save_path, 'w', encoding='utf-8'), ensure_ascii=False, indent=1) # indent=2 更加美观显示
# 由json文件构建COCO
def to_coco(self, json_path_list):
self._init_categories()
for json_path in json_path_list:
# print(type(json_path))
obj = self.read_jsonfile(json_path)
self.images.append(self._image(obj, json_path))
shapes = obj['shapes']
groupIds = []
for shape in shapes:
groupId = shape['group_id']
groupIds.append(groupId)
for i in set(groupIds):
keyPoints = [0] * 51
keyPointNum = 0
bbox = []
for shape in shapes:
if i != shape['group_id']:
continue
if shape['shape_type'] == "point":
labelNum = int(shape['label'])
keyPoints[labelNum * 3 + 0] = int(shape['points'][0][0] + 0.5)
keyPoints[labelNum * 3 + 1] = int(shape['points'][0][1] + 0.5)
keyPoints[labelNum * 3 + 2] = 2
keyPointNum += 1
if shape['shape_type'] == 'rectangle':
x0, y0, x1, y1 = shape['points'][0][0], shape['points'][0][1], \
shape['points'][1][0], shape['points'][1][1]
xmin = min(x0, x1)
ymin = min(y0, y1)
xmax = max(x0, x1)
ymax = max(y0, y1)
bbox = [xmin, ymin, xmax - xmin, ymax - ymin]
annotation = self._annotation(bbox, keyPoints, keyPointNum)
self.annotations.append(annotation)
self.ann_id += 1
self.img_id += 1
# for shape in shapes:
# label = shape['label']
# if label != 'person':
# continue
#
# annotation = self._annotation(shape)
# self.annotations.append(annotation)
# self.ann_id += 1
# self.img_id += 1
instance = {}
instance['info'] = 'spytensor created'
instance['license'] = ['license']
instance['images'] = self.images
instance['annotations'] = self.annotations
instance['categories'] = self.categories
return instance
# 构建类别
def _init_categories(self):
for k, v in classname_to_id.items():
category = {}
category['id'] = v
category['name'] = k
self.categories.append(category)
# 构建COCO的image字段
def _image(self, obj, jsonPath):
image = {}
# img_x = utils.img_b64_to_arr(obj['imageData'])
# h, w = img_x.shape[:-1]
jpgPath = jsonPath.replace('.json', '.jpg')
jpgData = cv2.imread(jpgPath)
h, w, _ = jpgData.shape
image['height'] = h
image['width'] = w
image['id'] = self.img_id
# image['file_name'] = os.path.basename(jsonPath).replace(".json", ".jpg")
image['file_name'] = jpgPath.split(self.splitDir)[-1].replace('\\', '/')
return image
# 构建COCO的annotation字段
def _annotation(self, bbox, keyPoints, keyNum):
annotation = {}
annotation['id'] = self.ann_id
annotation['image_id'] = self.img_id
annotation['category_id'] = 1
# annotation['segmentation'] = [np.asarray(points).flatten().tolist()]
annotation['segmentation'] = []
annotation['bbox'] = bbox
annotation['iscrowd'] = 0
annotation['area'] = bbox[2] * bbox[3]
annotation['keypoints'] = keyPoints
annotation['num_keypoints'] = keyNum
return annotation
# 读取json文件，返回一个json对象
def read_jsonfile(self, path):
with open(path, "r", encoding='utf-8') as f:
return json.load(f)
# COCO的格式： [x1,y1,w,h] 对应COCO的bbox格式
def _get_box(self, points):
min_x = min_y = np.inf
max_x = max_y = 0
for x, y in points:
min_x = min(min_x, x)
min_y = min(min_y, y)
max_x = max(max_x, x)
max_y = max(max_y, y)
return [min_x, min_y, max_x - min_x, max_y - min_y]
if __name__ == '__main__':
labelme_path = r"G:\XRW\Data\selfjson"
print(labelme_path)
jsonName = labelme_path.split('\\')[-1]
saved_coco_path = r"G:\XRW\Data\mycoco"
print(saved_coco_path)
#####################################
# 这个一定要注意
# 为了方便合入coco数据， 定义截断文件的文件夹与文件名字
splitDirFlag = 'labelMePoint\\'
######################################
# 创建文件
if not os.path.exists("%s/annotations/"%saved_coco_path):
os.makedirs("%s/annotations/"%saved_coco_path)
json_list_path = glob.glob(os.path.join(labelme_path, '*.json'))
train_path, val_path = json_list_path, ''
# print(train_path)
print("train_n:", len(train_path), 'val_n:', len(val_path))
# 把训练集转化为COCO的json格式
l2c_train = Lableme2CoCo(splitDirFlag)
# print(train_path)
train_instance = l2c_train.to_coco(train_path)
l2c_train.save_coco_json(train_instance, '%s/annotations/%s.json'%(saved_coco_path, jsonName))
labelme_path：自己标注的json文件路径
saved_coco_path：生成的CoCo格式保存位置
运行代码得到
2、将生成的CoCo格式的Json文件转换成Yolov8-Pose格式的txt文件
utils.py
import glob
import os
import shutil
from pathlib import Path
import numpy as np
from PIL import ExifTags
from tqdm import tqdm
# Parameters
img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes
vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == 'Orientation':
break
def exif_size(img):
# Returns exif-corrected PIL size
s = img.size # (width, height)
try:
rotation = dict(img._getexif().items())[orientation]
if rotation in [6, 8]: # rotation 270
s = (s[1], s[0])
except:
pass
return s
def split_rows_simple(file='../data/sm4/out.txt'): # from utils import *; split_rows_simple()
# splits one textfile into 3 smaller ones based upon train, test, val ratios
with open(file) as f:
lines = f.readlines()
s = Path(file).suffix
lines = sorted(list(filter(lambda x: len(x) > 0, lines)))
i, j, k = split_indices(lines, train=0.9, test=0.1, validate=0.0)
for k, v in {'train': i, 'test': j, 'val': k}.items(): # key, value pairs
if v.any():
new_file = file.replace(s, f'_{k}{s}')
with open(new_file, 'w') as f:
f.writelines([lines[i] for i in v])
def split_files(out_path, file_name, prefix_path=''): # split training data
file_name = list(filter(lambda x: len(x) > 0, file_name))
file_name = sorted(file_name)
i, j, k = split_indices(file_name, train=0.9, test=0.1, validate=0.0)
datasets = {'train': i, 'test': j, 'val': k}
for key, item in datasets.items():
if item.any():
with open(f'{out_path}_{key}.txt', 'a') as file:
for i in item:
file.write('%s%s\n' % (prefix_path, file_name[i]))
def split_indices(x, train=0.9, test=0.1, validate=0.0, shuffle=True): # split training data
n = len(x)
v = np.arange(n)
if shuffle:
np.random.shuffle(v)
i = round(n * train) # train
j = round(n * test) + i # test
k = round(n * validate) + j # validate
return v[:i], v[i:j], v[j:k] # return indices
def make_dirs(dir='new_dir/'):
# Create folders
dir = Path(dir)
if dir.exists():
shutil.rmtree(dir) # delete dir
for p in dir, dir / 'labels', dir / 'images':
p.mkdir(parents=True, exist_ok=True) # make dir
return dir
def write_data_data(fname='data.data', nc=80):
# write darknet *.data file
lines = ['classes = %g\n' % nc,
'train =../out/data_train.txt\n',
'valid =../out/data_test.txt\n',
'names =../out/data.names\n',
'backup = backup/\n',
'eval = coco\n']
with open(fname, 'a') as f:
f.writelines(lines)
def image_folder2file(folder='images/'): # from utils import *; image_folder2file()
# write a txt file listing all imaged in folder
s = glob.glob(f'{folder}*.*')
with open(f'{folder[:-1]}.txt', 'w') as file:
for l in s:
file.write(l + '\n') # write image list
def add_coco_background(path='../data/sm4/', n=1000): # from utils import *; add_coco_background()
# add coco background to sm4 in outb.txt
p = f'{path}background'
if os.path.exists(p):
shutil.rmtree(p) # delete output folder
os.makedirs(p) # make new output folder
# copy images
for image in glob.glob('../coco/images/train2014/*.*')[:n]:
os.system(f'cp {image} {p}')
# add to outb.txt and make train, test.txt files
f = f'{path}out.txt'
fb = f'{path}outb.txt'
os.system(f'cp {f} {fb}')
with open(fb, 'a') as file:
file.writelines(i + '\n' for i in glob.glob(f'{p}/*.*'))
split_rows_simple(file=fb)
def create_single_class_dataset(path='../data/sm3'): # from utils import *; create_single_class_dataset('../data/sm3/')
# creates a single-class version of an existing dataset
os.system(f'mkdir {path}_1cls')
def flatten_recursive_folders(path='../../Downloads/data/sm4/'): # from utils import *; flatten_recursive_folders()
# flattens nested folders in path/images and path/JSON into single folders
idir, jdir = f'{path}images/', f'{path}json/'
nidir, njdir = Path(f'{path}images_flat/'), Path(f'{path}json_flat/')
n = 0
# Create output folders
for p in [nidir, njdir]:
if os.path.exists(p):
shutil.rmtree(p) # delete output folder
os.makedirs(p) # make new output folder
for parent, dirs, files in os.walk(idir):
for f in tqdm(files, desc=parent):
f = Path(f)
stem, suffix = f.stem, f.suffix
if suffix.lower()[1:] in img_formats:
n += 1
stem_new = '%g_' % n + stem
image_new = nidir / (stem_new + suffix) # converts all formats to *.jpg
json_new = njdir / f'{stem_new}.json'
image = parent / f
json = Path(parent.replace('images', 'json')) / str(f).replace(suffix, '.json')
os.system("cp '%s' '%s'" % (json, json_new))
os.system("cp '%s' '%s'" % (image, image_new))
# cv2.imwrite(str(image_new), cv2.imread(str(image)))
print('Flattening complete: %g jsons and images' % n)
def coco91_to_coco80_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
None, 73, 74, 75, 76, 77, 78, 79, None]
return xslefjson2posetxt.py
import json
import cv2
import pandas as pd
from PIL import Image
from collections import defaultdict
from utils import *
def convert_coco_json(cocojsonpath, savepath,use_keypoints=False, cls91to80=True):
"""Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
Args:
labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
use_segments (bool, optional): Whether to include segmentation masks in the output.
use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
Raises:
FileNotFoundError: If the labels_dir path does not exist.
Example Usage:
convert_coco(labels_dir='../coco/annotations/', use_segments=True, use_keypoints=True, cls91to80=True)
Output:
Generates output files in the specified output directory.
"""
# save_dir = make_dirs('yolo_labels') # output directory
save_dir = make_dirs(savepath) # output directory
coco80 = coco91_to_coco80_class()
# Import json
for json_file in sorted(Path(cocojsonpath).resolve().glob('*.json')):
fn = Path(save_dir) / 'labels' / json_file.stem.replace('instances_', '') # folder name
fn.mkdir(parents=True, exist_ok=True)
with open(json_file) as f:
data = json.load(f)
# Create image dict
images = {f'{x["id"]:d}': x for x in data['images']}
# Create image-annotations dict
imgToAnns = defaultdict(list)
for ann in data['annotations']:
imgToAnns[ann['image_id']].append(ann)
# Write labels file
for img_id, anns in tqdm(imgToAnns.items(), desc=f'Annotations {json_file}'):
img = images[f'{img_id:d}']
h, w, f = img['height'], img['width'], img['file_name']
bboxes = []
segments = []
keypoints = []
for ann in anns:
if ann['iscrowd']:
continue
# The COCO box format is [top left x, top left y, width, height]
box = np.array(ann['bbox'], dtype=np.float64)
box[:2] += box[2:] / 2 # xy top-left corner to center
box[[0, 2]] /= w # normalize x
box[[1, 3]] /= h # normalize y
if box[2] <= 0 or box[3] <= 0: # if w <= 0 and h <= 0
continue
cls = coco80[ann['category_id'] - 1] if cls91to80 else ann['category_id'] - 1 # class
box = [cls] + box.tolist()
if box not in bboxes:
bboxes.append(box)
if use_keypoints and ann.get('keypoints') is not None:
k = (np.array(ann['keypoints']).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
k = box + k
keypoints.append(k)
# Write
fname = f.split('/')[-1]
# with open((fn / f).with_suffix('.txt'), 'a') as file:
with open((fn / fname).with_suffix('.txt'), 'a') as file:
for i in range(len(bboxes)):
if use_keypoints:
line = *(keypoints[i]), # cls, box, keypoints
file.write(('%g ' * len(line)).rstrip() % line + '\n')
if __name__ == '__main__':
source = 'COCO'
cocojsonpath = r'G:\XRW\Data\mycoco\annotations'
savepath = r'G:\XRW\Data\myposedata'
if source == 'COCO':
convert_coco_json(cocojsonpath, # directory with *.json
savepath,
use_keypoints=True,
cls91to80=True)运行代码得到：
<class-index>是对象的类的索引，<x> <y> <width> <height>是边界框的坐标，<px1> <py1> <px2> <py2> ... <pxn> <pyn>是关键点的像素坐标。坐标由空格分隔。
3、检查生成的txt是否准确
PoseVisualization.py：将txt的信息可视化在图片上进行验证。
import cv2
imgpath = r'G:\XRW\Data\selfjson\five_22101205_000930.jpg'
txtpath = r'G:\XRW\Data\myposedata\labels\selfjson\five_22101205_000930.txt'
f = open(txtpath,'r')
lines = f.readlines()
img = cv2.imread(imgpath)
h, w, c = img.shape
colors = [[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
[153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]]
for line in lines:
print(line)
l = line.split(' ')
print(len(l))
cx = float(l[1]) * w
cy = float(l[2]) * h
weight = float(l[3]) * w
height = float(l[4]) * h
xmin = cx - weight/2
ymin = cy - height/2
xmax = cx + weight/2
ymax = cy + height/2
print((xmin,ymin),(xmax,ymax))
cv2.rectangle(img,(int(xmin),int(ymin)),(int(xmax),int(ymax)),(0,255,0),2)
kpts = []
for i in range(17):
x = float(l[5:][3*i]) * w
y = float(l[5:][3*i+1]) * h
s = int(l[5:][3*i+2])
print(x,y,s)
if s != 0:
cv2.circle(img,(int(x),int(y)),1,colors[i],2)
kpts.append([int(x),int(y),int(s)])
print(kpts)
kpt_line = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
[8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
for j in range(len(kpt_line)):
m,n = kpt_line[j][0],kpt_line[j][1]
if kpts[m-1][2] !=0 and kpts[n-1][2] !=0:
cv2.line(img,(kpts[m-1][0],kpts[m-1][1]),(kpts[n-1][0],kpts[n-1][1]),colors[j],2)
img = cv2.resize(img, None, fx=0.5, fy=0.5)
cv2.imshow('1',img)
cv2.waitKey(0)
这样就将自己的Json格式转成训练Yolov8-Pose姿态的txt格式了。
4、将图片copy到对应路径中
以上步骤完成后只生成了txt，需要再将对应的图片copy到对应路径中。
pickImg.py
import glob
import os
import shutil
imgpath = r'G:\XRW\Data\selfjson'
txtpath = r'G:\XRW\Data\myposedata\labels\selfjson'
savepath = r'G:\XRW\Data\myposedata\images\selfjson'
os.makedirs(savepath,exist_ok=True)
imglist = glob.glob(os.path.join(imgpath ,'*.jpg'))
# print(imglist)
txtlist = glob.glob(os.path.join(txtpath ,'*.txt'))
# print(txtlist)
for img in imglist:
name = txtpath + '\\' +img.split('\\')[-1].split('.')[0 ] +'.txt'
print(name)
if name in txtlist:
shutil.copy(img ,savepath)- imgpath CoCo数据集图片路径
- txtpath 生成的txt路径
- savepath 保存图片的路径
这样就将自己标注的数据集转换成Yolov8-Pose格式的txt了。
